> ## Documentation Index > Fetch the complete documentation index at: https://praison.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Export a trained model > Publish an already-trained model to Hugging Face, GGUF, or Ollama without re-running training Publish a model you already trained to Hugging Face, GGUF, or Ollama — no re-training, one command. ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph LR subgraph "Export a trained model" M[lora_model/] --> T{Target?} T -->|hf| HF[Hugging Face
merged 16-bit] T -->|gguf| GG[GGUF
quantized] T -->|ollama| OL[Ollama
ollama.com] end classDef input fill:#8B0000,stroke:#7C90A0,color:#fff classDef question fill:#F59E0B,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class M input class T question class HF,GG,OL output ``` ## Quick Start You already have a trained model in `lora_model/`. Pick a target and publish. Merge and push FP16 weights to a Hub repo. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai-train export hf --model-dir lora_model --hf me/my-model ``` Write a local `.gguf` — add `--hf` to also push it to the Hub. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} # Local .gguf only praisonai-train export gguf --model-dir lora_model --quant q4_k_m # Local .gguf + push to the Hub praisonai-train export gguf --model-dir lora_model --hf me/my-model --quant q4_k_m ``` Create and push a quantized model to `ollama.com`. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} praisonai-train export ollama --model-dir lora_model --ollama me/my-model --quant q4_k_m ``` *** ## When to use this You already trained a model (e.g. `lora_model/` exists) and don't want to spend an hour re-running training just to publish it to a different target, fix a `chat_template`, or try a different quantization. `export` loads the trained weights once and publishes them. *** ## How It Works ```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} graph TB Cfg[--model-dir + flags] --> Load[TrainModel.for_export] Load --> Base[Infer base model
for chat template] Base --> Pub{Target} Pub -->|hf| M[save_model_merged] Pub -->|gguf| G[save_model_gguf
+ optional push] Pub -->|ollama| O[create_and_push_ollama_model] classDef input fill:#8B0000,stroke:#7C90A0,color:#fff classDef process fill:#189AB4,stroke:#7C90A0,color:#fff classDef question fill:#F59E0B,stroke:#7C90A0,color:#fff classDef output fill:#10B981,stroke:#7C90A0,color:#fff class Cfg input class Load,Base process class Pub question class M,G,O output ``` Export skips the training-only preflight, so it runs on CPU — there's nothing to fine-tune. The base model name drives chat-template selection so the exported model isn't garbled. *** ## CLI flags | Flag | Short | Purpose | Default | | --------------------- | ----- | ------------------------------------------------------------------ | -------- | | `TARGET` (positional) | — | `ollama` \| `gguf` \| `hf` | required | | `--model-dir` | `-d` | Directory of the already-trained model | required | | `--config` | `-c` | Optional `config.yaml` for extra export knobs | — | | `--ollama` | — | Ollama model name, e.g. `me/my-model` | — | | `--hf` | — | Hugging Face repo id, e.g. `me/my-model` | — | | `--quant` | — | Quantization method for gguf/ollama, e.g. `q4_k_m` | — | | `--base-model` | — | Base model id (for chat-template selection) | inferred | | `--mtp-draft` | — | Also fetch the stock MTP drafter (Gemma-4 only) for fast inference | `false` | `gguf` always writes a **local** `.gguf` (so you can serve it). Add `--hf me/my-model` to additionally push it to the Hub. Without `--hf`, the GGUF is written under `/gguf`. *** ## How the base model is inferred The base model name selects the chat template, so the exported model formats prompts correctly. Resolution order: 1. `--base-model` if passed. 2. else `model_name` in `--config`. 3. else `_name_or_path` from `/config.json`. 4. else the model-dir name itself. *** ## Prerequisites `HF_TOKEN` env var **or** a cached `huggingface-cli login`. The token must have **write** scope and the repo must be under your own username. ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} export HF_TOKEN=hf_... # or huggingface-cli login ``` * `ollama` CLI installed (from [https://ollama.com](https://ollama.com)). * Your signing key registered at [https://ollama.com/settings/keys](https://ollama.com/settings/keys) — public key lives at `~/.ollama/id_ed25519.pub`. * Enough free space on `$OLLAMA_MODELS` (defaults to `~/.ollama/models`). CUDA is **not** required for export-only. CPU is fine because there is nothing to fine-tune. *** ## Valid `--quant` values Any of these quantization methods (from `VALID_QUANTIZATION_METHODS`): ``` q2_k q3_k_m q4_0 q4_1 q4_k_m q5_0 q5_1 q5_k_m q6_k q8_0 f16 bf16 ``` A typo like `q4km` fails fast with the full list of valid choices — no long run wasted. *** ## Failure modes → clean errors Every known failure prints a single actionable message instead of a raw traceback. | You'll see | What happened | Fix | | ----------------------------------------------------------------------------- | -------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | | `Hugging Face rejected the token (401)...` | HF token invalid/expired | `huggingface-cli login` or `export HF_TOKEN=hf_...` with a **write** token | | `No write access to '' (403)...` | Repo not under your username, or token lacks write scope | Set `--hf me/my-model` (your username); regenerate a write token | | `Ollama rejected the push (unauthorized)...` (your public key printed inline) | Ollama signing key not registered | Add the printed `~/.ollama/id_ed25519.pub` key at [https://ollama.com/settings/keys](https://ollama.com/settings/keys) | | `Not enough disk for the Ollama model: X GB free on '...', need ~Y GB` | `$OLLAMA_MODELS` volume too small | `export OLLAMA_MODELS=/big/disk/ollama-models` | | `ollama serve did not become ready in 15 seconds. ollama serve output: ...` | Daemon failed to start (port busy, bad `OLLAMA_HOST`) | Inspect the stderr shown in the message | | `quantization_method 'q4km' is not valid. Choose one of: ...` | Typo in `--quant` | Pick a value from the list above | The readiness probe honours `OLLAMA_HOST` and confirms `GET /api/version` — a healthy remote or non-default daemon is used instead of being ignored. *** ## Python API Script exports with the `TrainModel.for_export()` classmethod — it builds an export-only trainer (no dataset required) and validates the quantization up front. ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}} from praisonai_train.train.llm.trainer import TrainModel trainer = TrainModel.for_export({ "final_model_dir": "lora_model", "model_name": "unsloth/gemma-2-2b-it-bnb-4bit", "hf_model_name": "me/my-model", "quantization_method": "q4_k_m", }) model, tokenizer = trainer.load_model() trainer.model, trainer.hf_tokenizer = model, tokenizer trainer.save_model_merged() # Hugging Face merged 16-bit # trainer.save_model_gguf() # local GGUF # trainer.push_model_gguf() # push GGUF to the Hub # trainer.create_and_push_ollama_model() # Ollama ``` *** ## Best Practices Set `--hf me/my-model` (not a bare name). A namespaced repo id is treated as a Hub target, never a local directory to delete. Pass `--quant q4_k_m` so the model is packed small. Ollama exports run `ollama create --quantize`, avoiding a huge FP16 intermediate. If the exported model's output looks garbled, pass `--base-model` explicitly (e.g. `unsloth/gemma-2-2b-it-bnb-4bit`) so the right chat template is used. *** ## Related Full fine-tuning flow and config.yaml reference. Every `praisonai train` subcommand, including `export`. Install matrix for the standalone trainer. Fine-tune across every GPU with torchrun.